End-to-End Generation of City-Scale Vectorized Maps by Crowdsourced Vehicles
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909686084665344 |
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| author | Feng, Zebang Fan, Miao Liu, Bao Xu, Shengtong Xiong, Haoyi |
| author_facet | Feng, Zebang Fan, Miao Liu, Bao Xu, Shengtong Xiong, Haoyi |
| contents | High-precision vectorized maps are indispensable for autonomous driving, yet traditional LiDAR-based creation is costly and slow, while single-vehicle perception methods lack accuracy and robustness, particularly in adverse conditions. This paper introduces EGC-VMAP, an end-to-end framework that overcomes these limitations by generating accurate, city-scale vectorized maps through the aggregation of data from crowdsourced vehicles. Unlike prior approaches, EGC-VMAP directly fuses multi-vehicle, multi-temporal map elements perceived onboard vehicles using a novel Trip-Aware Transformer architecture within a unified learning process. Combined with hierarchical matching for efficient training and a multi-objective loss, our method significantly enhances map accuracy and structural robustness compared to single-vehicle baselines. Validated on a large-scale, multi-city real-world dataset, EGC-VMAP demonstrates superior performance, enabling a scalable, cost-effective solution for city-wide mapping with a reported 90\% reduction in manual annotation costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08901 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | End-to-End Generation of City-Scale Vectorized Maps by Crowdsourced Vehicles Feng, Zebang Fan, Miao Liu, Bao Xu, Shengtong Xiong, Haoyi Robotics High-precision vectorized maps are indispensable for autonomous driving, yet traditional LiDAR-based creation is costly and slow, while single-vehicle perception methods lack accuracy and robustness, particularly in adverse conditions. This paper introduces EGC-VMAP, an end-to-end framework that overcomes these limitations by generating accurate, city-scale vectorized maps through the aggregation of data from crowdsourced vehicles. Unlike prior approaches, EGC-VMAP directly fuses multi-vehicle, multi-temporal map elements perceived onboard vehicles using a novel Trip-Aware Transformer architecture within a unified learning process. Combined with hierarchical matching for efficient training and a multi-objective loss, our method significantly enhances map accuracy and structural robustness compared to single-vehicle baselines. Validated on a large-scale, multi-city real-world dataset, EGC-VMAP demonstrates superior performance, enabling a scalable, cost-effective solution for city-wide mapping with a reported 90\% reduction in manual annotation costs. |
| title | End-to-End Generation of City-Scale Vectorized Maps by Crowdsourced Vehicles |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.08901 |